Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses and provide critical vessel parameters to optimize percutaneous intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) simultaneously provides high-resolution cross-sectional images of vascular structures while also revealing preponderant tissue components such as collagen and smooth muscle and thereby enhances plaque characterization. Automated interpretation of these features promises to facilitate the objective clinical investigation of the natural history and significance of coronary atheromas. Here, we propose a convolutional neural network model, optimized using a new multi-term loss function, to classify the lumen, intima, and media layers in addition to the guidewire and plaque shadows. We demonstrate that our multi-class classification model outperforms state-of-the-art methods in detecting the coronary anatomical layers. Furthermore, the proposed model segments two classes of common imaging artifacts and detects the anatomical layers within the thickened vessel wall regions that were excluded from analysis by other studies. The source code and the trained model are publicly available at https://github.com/mhaft/OCTseg
翻译:心血管超声波和光学一致性断层成像仪可广泛使用,用于鉴定冠状动脉的内脏,并提供关键容器参数,以优化腹腔干预; 内穿两极分性敏感光学一致性断层摄影(PS-OCT)同时提供血管结构的高分辨率截面图像,同时还显示血管结构的高分辨率截面图象,同时暴露主要组织组成部分,如科兰根和光滑肌肉,从而增强立体特征; 对这些特征的自动解释将有利于对冠状动脉动的自然历史和重大意义的客观临床调查; 在此,我们提议采用一个脉动神经网络模型,优化使用新的多期损失功能,除导线和广场阴影外,还要对润滑、直径和媒体层进行分类; 我们证明,我们的多级分类模型在检测共振动层层层层结构时,超越了最先进的方法; 此外,拟议的普通成像工艺品的两个模型部分和探测较厚的船舶壁壁壁内层的剖面层。